bioRxivCancer BiologyPreprint
A critical role of protein damage response in mediating cancer drug resistance
Fangyuan ShaoiD, Jie LiiD, Hao XiaoiD, Ling Li, Bo Li, Y H Chen, Xiangpeng ChuiD, Maoxin RaniD, Dongyang TangiD, Yuzhong PengiD, Yujian Huang, Lijian WangiD, Yanxia ShiiD, Nan ShaoiD, Kai MiaoiD, Changhua ZhangiD, Ying Ni LiniD, Jun YaniD, Kin Yip TamiD, Xiaoling XuiD, Chu‐Xia DengiD
Abstract
Multidrug resistance (MDR) frequently occurs during cancer therapy and remains a major obstacle for the cure of most cancers(1, 2). Drug resistance could exist intrinsically or be acquired by drug treatment(3-5), yet factors that regulate the resistance remain elusive. Here, we show that most anticancer drugs damage neosynthesized proteins prior to reaching their canonic targets and elicit profound cytotoxicity, which is largely compensated by protein damage response (PDR). We demonstrate that the PDR includes damage recognition and clearance that are mainly mediated by ubiquitin and proteasome systems, although some other factors, including cellular ATP levels and proliferation status, are also involved. We show that cancer stem cells (CSCs), which have lower protein synthesis, and drug resistance acquired cells (DRAC), which have higher proteasome activity, are more resistant than other cells. We further demonstrate that ATP promotes protein synthesis and suppresses proteasome activity, thus, increasing mitochondrial ATP production by PDK1 inhibition and using proteasome inhibitor to block protein damage clearance render CSCs and DRACs more vulnerable to anticancer drugs. Thus, patients with drug-resistant cancers and treatment-naive patients with low ATP levels and/or high proteasome activity can be identified and subtyped, and therapies containing PDK1-I and/or proteasome-I may be effective options for these patients.
- organism: human
- assay: proteomics
- research focus: cancer & tumor microenvironment
- research focus: drug response / drug discovery
- research focus: cell type annotation
- approach: experimental assay
- approach: statistical model
- Citations
- 1
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